Pydantic AI

pip install pydantic-ai

# Install extras for specific providers
pip install 'pydantic-ai[openai]'       # OpenAI / Azure OpenAI
pip install 'pydantic-ai[anthropic]'    # Anthropic Claude
pip install 'pydantic-ai[gemini]'       # Google Gemini
pip install 'pydantic-ai[groq]'         # Groq
pip install 'pydantic-ai[vertexai]'     # Google Vertex AI

from pydantic_ai import Agent

# Simple agent — returns a plain string
agent = Agent(
    'anthropic:claude-sonnet-4-6',
    system_prompt='You are a helpful assistant. Be concise.',
)

result = agent.run_sync('What is the capital of Japan?')
print(result.data)  # "Tokyo"
print(result.usage())  # Usage(requests=1, request_tokens=..., response_tokens=...)

from pydantic import BaseModel
from pydantic_ai import Agent

class MovieReview(BaseModel):
    title: str
    year: int
    rating: float  # 0.0 to 10.0
    summary: str
    recommended: bool

agent = Agent(
    'openai:gpt-4o',
    result_type=MovieReview,
    system_prompt='You are a film critic. Return structured reviews.',
)

result = agent.run_sync('Review Inception (2010)')
review = result.data  # Fully typed MovieReview instance
print(f"{review.title} ({review.year}): {review.rating}/10")
print(f"Recommended: {review.recommended}")

from pydantic_ai import Agent, RunContext
from pydantic import BaseModel
import httpx

class WeatherReport(BaseModel):
    city: str
    temperature_c: float
    condition: str

weather_agent = Agent(
    'anthropic:claude-sonnet-4-6',
    result_type=WeatherReport,
    system_prompt='Get current weather for the requested city.',
)

@weather_agent.tool
async def get_temperature(ctx: RunContext, city: str) -> dict:
    """Fetch the current temperature for a city from the weather API."""
    async with httpx.AsyncClient() as client:
        r = await client.get(f'https://wttr.in/{city}?format=j1')
        data = r.json()
        return {
            'temp_c': float(data['current_condition'][0]['temp_C']),
            'description': data['current_condition'][0]['weatherDesc'][0]['value'],
        }

import asyncio
result = asyncio.run(weather_agent.run('What is the weather in Tokyo?'))
print(result.data)

from dataclasses import dataclass
from pydantic_ai import Agent, RunContext
from pydantic import BaseModel

@dataclass
class Deps:
    db: Database
    user_id: str

class SupportResponse(BaseModel):
    message: str
    escalate: bool

support_agent = Agent(
    'openai:gpt-4o-mini',
    deps_type=Deps,
    result_type=SupportResponse,
    system_prompt='You are a support agent. Use the tools to help customers.',
)

@support_agent.tool
async def get_order_history(ctx: RunContext[Deps]) -> list[dict]:
    """Fetch recent orders for the current user."""
    return await ctx.deps.db.get_orders(ctx.deps.user_id, limit=5)

@support_agent.tool
async def create_refund(ctx: RunContext[Deps], order_id: str, reason: str) -> dict:
    """Initiate a refund for a specific order."""
    return await ctx.deps.db.create_refund(order_id, reason, ctx.deps.user_id)

# Usage
async def handle_support(user_id: str, message: str):
    deps = Deps(db=get_db(), user_id=user_id)
    result = await support_agent.run(message, deps=deps)
    return result.data

from pydantic_ai.models.test import TestModel

def test_support_agent_escalates():
    with support_agent.override(model=TestModel()):
        # TestModel returns a minimal valid response matching result_type
        result = support_agent.run_sync(
            'I want to cancel my account',
            deps=Deps(db=FakeDb(), user_id='user-123'),
        )
    # Test the structure, not the LLM's exact words
    assert isinstance(result.data, SupportResponse)
    assert isinstance(result.data.escalate, bool)

from pydantic_ai.models.function import FunctionModel, ModelContext

def my_model(messages, info):
    return ModelResponse(parts=[TextPart('Always this response')])

with agent.override(model=FunctionModel(my_model)):
    result = agent.run_sync('anything')

import asyncio
from pydantic_ai import Agent

agent = Agent('anthropic:claude-sonnet-4-6')

async def stream_response():
    async with agent.run_stream('Write a haiku about Python') as result:
        async for chunk in result.stream_text():
            print(chunk, end='', flush=True)
    print()  # newline
    print(f"Total tokens: {result.usage()}")

asyncio.run(stream_response())

from pydantic_ai import Agent
from pydantic_ai.messages import ModelMessagesTypeAdapter

agent = Agent('openai:gpt-4o', system_prompt='You are a helpful assistant.')

# First turn
result1 = agent.run_sync('My name is Alice.')
history = result1.all_messages()

# Second turn — passes conversation history
result2 = agent.run_sync('What is my name?', message_history=history)
print(result2.data)  # "Your name is Alice."

from pydantic import BaseModel, Field
from pydantic_ai import Agent
from typing import Literal

class CodeReview(BaseModel):
    quality: Literal['excellent', 'good', 'needs_work', 'poor']
    issues: list[str] = Field(default_factory=list)
    suggestions: list[str] = Field(default_factory=list)
    approved: bool

code_review_agent = Agent(
    'anthropic:claude-sonnet-4-6',
    result_type=CodeReview,
    system_prompt="""
    You are a senior engineer performing code review.
    Evaluate code quality, identify issues, and provide actionable suggestions.
    Set approved=True only for good or excellent quality code with no security issues.
    """,
)

def review_code(diff: str) -> CodeReview:
    result = code_review_agent.run_sync(f"Review this code:\n\n{diff}")
    return result.data

from pydantic_ai import Agent, ModelRetry
from pydantic import BaseModel, field_validator

class StrictJson(BaseModel):
    value: int

    @field_validator('value')
    def must_be_positive(cls, v):
        if v <= 0:
            raise ValueError('value must be positive')
        return v

agent = Agent('openai:gpt-4o-mini', result_type=StrictJson)

@agent.result_validator
async def validate_result(ctx, result: StrictJson) -> StrictJson:
    if result.value > 1000:
        raise ModelRetry('Value must be under 1000. Try again with a smaller number.')
    return result

from pydantic_ai import Agent
from pydantic import BaseModel

class ResearchSummary(BaseModel):
    key_points: list[str]
    conclusion: str

class BlogPost(BaseModel):
    title: str
    body: str
    meta_description: str

researcher = Agent('openai:gpt-4o', result_type=ResearchSummary)
writer = Agent('anthropic:claude-sonnet-4-6', result_type=BlogPost)

async def research_and_write(topic: str) -> BlogPost:
    # Stage 1: research
    research = await researcher.run(f'Research the topic: {topic}')

    # Stage 2: write based on research
    post = await writer.run(
        f'Write a blog post about: {topic}\n\nResearch:\n' +
        '\n'.join(f'- {p}' for p in research.data.key_points) +
        f'\n\nConclusion: {research.data.conclusion}'
    )
    return post.data